class_docstr: Dataset iterator for fitting estimator models, like vocabs, kNN, vectorizers.
  Data is passed as a list of strings(documents).
  Generate batches (for large datasets).
  
  Args:
      data: list of documents
      doc_ids: provided document ids
      seed: random seed for data shuffling
      shuffle: whether to shuffle data during batching
  
  Attributes:
      shuffle: whether to shuffle data during batching
      random: instance of :class:`Random` initialized with a seed
      data: list of documents
      doc_ids: provided by a user ids or generated automatically ids
docstr: Get all data
docstr_tok: ["Get","all","data"]
code: def get_instances(self):
      """Get all data"""
      doc_ids = list(self.doc_ids)
      docs = [self.get_doc_content(doc_id) for doc_id in doc_ids]
      return docs, doc_ids
  
file_path: DeepPavlov/deeppavlov/core/data/data_fitting_iterator.py
imports: from logging import getLogger
  from random import Random
  from typing import List, Generator, Tuple, Any, Optional
  from deeppavlov.core.common.registry import register
label_desc: Use image node_id 5 for calling the DataFittingIterator obj's underlying member method code with example usage: obj.get_instances() and returns: docs,  doc_ids